代码搜索结果

function a = predict_performance(algorithm, algorithm_params, features, targets, region) % Predict the final performance of an algorithm from the learning curves % Inputs: % algorithm

function [bel, converged, belpairs, msgs] = inference(adj,lambda,local,algorithm_name,varargin) % [bel, convereged] = inference(adj,lambda,local,algorithm_name,varargin) % makes inference in the given

function a = predict_performance(algorithm, algorithm_params, features, targets, region) % Predict the final performance of an algorithm from the learning curves % Inputs: % algorithm

function a = predict_performance(algorithm, algorithm_params, features, targets, region) % Predict the final performance of an algorithm from the learning curves % Inputs: % algorithm

function a = predict_performance(algorithm, algorithm_params, features, targets, region) % Predict the final performance of an algorithm from the learning curves % Inputs: % algorithm

function a = predict_performance(algorithm, algorithm_params, features, targets, region) % Predict the final performance of an algorithm from the learning curves % Inputs: % algorithm

#!/bin/sh ################################################################################ # # ipsecvpn -- script for use on a roadwarrior to start/stop a tunnel-mode # IPSEC connec

// file: $isip/class/algo/Kernel/kern_00.cc // version: $Id: kern_00.cc,v 1.1 2001/07/20 15:51:54 hamaker Exp $ // // isip include files // #include "Kernel.h" //------------------------------------

# $KAME: racoon.conf.sample-gssapi,v 1.5 2001/08/16 06:33:40 itojun Exp $ # sample configuration for GSSAPI authentication (basically, kerberos). # doc/README.gssapi gives some idea on how to configu

/* Copyright (c) 1994 Sun Wu, Udi Manber, Burra Gopal. All Rights Reserved. */ Started in Feb 1991. This chronicle briefly describes the progress of agrep. Feb/91: The approximate pattern matching a